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We explore techniques to significantly improve the compute efficiency andperformance of Deep Convolution Networks without impacting their accuracy. Toimprove the compute efficiency, we focus on achieving high accuracy withextremely low-precision (2-bit) weight networks, and to accelerate theexecution time, we aggressively skip operations on zero-values. We achieve thehighest reported accuracy of 76.6% Top-1/93% Top-5 on the Imagenet objectclassification challenge with low-precision network\footnote{github release ofthe source code coming soon} while reducing the compute requirement by ~3xcompared to a full-precision network that achieves similar accuracy.Furthermore, to fully exploit the benefits of our low-precision networks, webuild a deep learning accelerator core, dLAC, that can achieve up to 1TFLOP/mm^2 equivalent for single-precision floating-point operations (~2TFLOP/mm^2 for half-precision).

Accelerating Deep Convolutional Networks using low-precision and sparsity